CDF-RAG introduces a novel causal dynamic feedback mechanism that improves retrieval-augmented generation by focusing on causal relationships rather than mere semantic similarity.

• Addresses the critical limitation of conventional RAG systems that can't distinguish true causal relationships from spurious correlations
• Implements a feedback loop that dynamically adjusts retrieval strategy based on causal reasoning
• Achieves more factually accurate and causally consistent responses in knowledge-intensive tasks
• Reduces hallucinations and improves information trustworthiness

For security applications, this approach significantly enhances protection against misinformation by ensuring AI-generated content reflects true cause-and-effect relationships rather than misleading correlations, strengthening the reliability of AI systems in sensitive contexts.

CDF-RAG: Causal Dynamic Feedback for Adaptive Retrieval-Augmented Generation